Project Part 2

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This notebook contains the code samples found in Chapter 3, Section 5 of Deep Learning with R. Note that the original text features far more content, in particular further explanations and figures: in this notebook, you will only find source code and related comments.

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Data Exploration & Preparation

• Our goal in the second part of the assignment is to predict how good a (new) customer will pay back their credit card depts. In the data set application data from current customers (the first 18 attributes) together with their status (last attribute; target) are given.
  
• The attributes from the applications are

Attribute Name	Explanation	Remarks
ID	Client	number
CODE_GENDER	Gender
FLAG_OWN_CAR	Is there a car
FLAG_OWN_REALTY	Is there a property
CNT_CHILDREN	Number of children
AMT_INCOME_TOTAL	Annual income
NAME_INCOME_TYPE	Income category
NAME_EDUCATION_TYPE	Education level
NAME_FAMILY_STATUS	Marital status
NAME_HOUSING_TYPE	Way of living
DAYS_BIRTH	Birthday	Count backwards from current day (0), -1 means yesterday
DAYS_EMPLOYED	Start date of employment	Count backwards from current day(0). If positive, it means the person unemployed.
FLAG_MOBIL	Is there a mobile phone
FLAG_WORK_PHONE	Is there a work phone
FLAG_PHONE	Is there a phone
FLAG_EMAIL	Is there an email
OCCUPATION_TYPE	Occupation
CNT_FAM_MEMBERS	Family size

• The last attribute status contains the “pay-back behavior”, i.e. when did that customer pay back their depts:
  • 0: 1-29 days past due
  • 1: 30-59 days past due
  • 2: 60-89 days overdue
  • 3: 90-119 days overdue
  • 4: 120-149 days overdue
  • 5: Overdue or bad debts, write-offs for more than 150 days
  • C: paid off that month
  • X: No loan for the month Please note: We are learning only the pay-back behavior. The decision, i.e. if we accept a customer or not, is done in another process step – not here!

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Main task

• Design your network. Why did you use a feed-forward network, or a convolutional or recursive network – and why not?
  
• Use k-fold validation (with k = 10) to find the best hyperparameters for your network.
• Use the average of the accuracy to evaluate the performance of your trained network.
• Find a “reasonable” good model. Argue why that model is reasonable. If you are not able to find a reasonable good model, explain what you all did to find a good model and argue why you think that’s not a good model.
  
• Save your trained neural network with save_model_hdf5. Also save your data sets you used for training, testing and validation.

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Some hints

• Data preprocessing is easier here; no feature engineering is needed.
• You may be able to reuse parts of the exercises we used in our examples during lectures.
• All in- and output values need to be floating numbers (or integers in exceptions) in the range of [0,1].
• Please note that a neural network expects a R matrix or vector, not data frames. Transform your data (e.g. a data frame) into a matrix with data.matrix if needed.
  
• There are some models which show an accuracy higher than 90% (!) for training (and test) data – after learning more than 1000 epochs.

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Important notes

• Single-label, Multiclass classification problem on page 73 in Deep Learning with R
• Spaces must be removed in between ‘{r}' and '’, else an error with ‘’ will be produced
• K-Fold Validation on page 83ff and 94ff in Deep Learning with R
• Page 110, use Last-Layer activation softmax and loss function categorical_crossentropy
• Convolutional network ausgeschlossen, weil hauptsächlich Pattern recognition/image classification
• Recursive ausgeschlossen, weil hauptsächlich für TimeSeries-Vorhersagen verwendet, oder für Vorhersagen
• Feed-Forward, weil Classification-Task

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Data import

#install.packages("tidymodels")
library(here)
library(tidyverse)
library(ggplot2)
library(dplyr)
library(tensorflow)
library(tfdatasets)
library(tidymodels)
library(keras)
library(caret)
#LOAD DATA
setwd(getwd())
dataIn = "../Data/Dataset-part-2.csv"
data_in <- read.csv(dataIn,header = TRUE, sep =',')
#View(data_in)
data <- data.frame(data_in)
summary(data)

       ID          CODE_GENDER        FLAG_OWN_CAR       FLAG_OWN_REALTY     CNT_CHILDREN     AMT_INCOME_TOTAL 
 Min.   :5008804   Length:67614       Length:67614       Length:67614       Min.   : 0.0000   Min.   :  26100  
 1st Qu.:5465941   Class :character   Class :character   Class :character   1st Qu.: 0.0000   1st Qu.: 112500  
 Median :5954270   Mode  :character   Mode  :character   Mode  :character   Median : 0.0000   Median : 157500  
 Mean   :5908133                                                            Mean   : 0.4206   Mean   : 178867  
 3rd Qu.:6289080                                                            3rd Qu.: 1.0000   3rd Qu.: 225000  
 Max.   :7965248                                                            Max.   :19.0000   Max.   :6750000  
 NAME_INCOME_TYPE   NAME_EDUCATION_TYPE NAME_FAMILY_STATUS NAME_HOUSING_TYPE    DAYS_BIRTH     DAYS_EMPLOYED   
 Length:67614       Length:67614        Length:67614       Length:67614       Min.   :-25201   Min.   :-17531  
 Class :character   Class :character    Class :character   Class :character   1st Qu.:-19438   1st Qu.: -2886  
 Mode  :character   Mode  :character    Mode  :character   Mode  :character   Median :-15592   Median : -1305  
                                                                              Mean   :-15914   Mean   : 62022  
                                                                              3rd Qu.:-12347   3rd Qu.:  -321  
                                                                              Max.   : -7489   Max.   :365243  
   FLAG_MOBIL FLAG_WORK_PHONE    FLAG_PHONE       FLAG_EMAIL     OCCUPATION_TYPE    CNT_FAM_MEMBERS 
 Min.   :1    Min.   :0.0000   Min.   :0.0000   Min.   :0.0000   Length:67614       Min.   : 1.000  
 1st Qu.:1    1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.0000   Class :character   1st Qu.: 2.000  
 Median :1    Median :0.0000   Median :0.0000   Median :0.0000   Mode  :character   Median : 2.000  
 Mean   :1    Mean   :0.2028   Mean   :0.2742   Mean   :0.1005                      Mean   : 2.174  
 3rd Qu.:1    3rd Qu.:0.0000   3rd Qu.:1.0000   3rd Qu.:0.0000                      3rd Qu.: 3.000  
 Max.   :1    Max.   :1.0000   Max.   :1.0000   Max.   :1.0000                      Max.   :20.000  
    status         
 Length:67614      
 Class :character  
 Mode  :character  
                   
                   
                   

##Cleanup

# Check for duplicates 
sum(duplicated(data))

[1] 0

# No duplicates

#Remove ID (irrelevant) and FLAG_MOBIL (always 1)
data <- data %>% select(-ID, -FLAG_MOBIL)
cols <- c("CODE_GENDER","FLAG_OWN_CAR","FLAG_OWN_REALTY","NAME_INCOME_TYPE","NAME_EDUCATION_TYPE", "NAME_FAMILY_STATUS", "NAME_HOUSING_TYPE","FLAG_WORK_PHONE","FLAG_PHONE","FLAG_EMAIL", "OCCUPATION_TYPE","status")
cols

 [1] "CODE_GENDER"         "FLAG_OWN_CAR"        "FLAG_OWN_REALTY"     "NAME_INCOME_TYPE"   
 [5] "NAME_EDUCATION_TYPE" "NAME_FAMILY_STATUS"  "NAME_HOUSING_TYPE"   "FLAG_WORK_PHONE"    
 [9] "FLAG_PHONE"          "FLAG_EMAIL"          "OCCUPATION_TYPE"     "status"             

data[cols] <- lapply(data[cols],factor)

# Replacing empty values with "Unknown"
levels(data$OCCUPATION_TYPE) <- c(levels(data$OCCUPATION_TYPE), "Unknown")
data$OCCUPATION_TYPE[is.na(data$OCCUPATION_TYPE)] <- "Unknown"

# Replacing C and X in Status
levels(data$status)[levels(data$status)=="C"] <- "6"
#data$status[data$status == "X"] <- 7
levels(data$status)[levels(data$status)=="X"] <- "7"
# #Convert factors into numericals
# data %<>% mutate_if(is.factor, as.numeric)

summary(data)

 CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY  CNT_CHILDREN     AMT_INCOME_TOTAL              NAME_INCOME_TYPE
 F:43924     N:43107      N:21090         Min.   : 0.0000   Min.   :  26100   Commercial associate:15640  
 M:23690     Y:24507      Y:46524         1st Qu.: 0.0000   1st Qu.: 112500   Pensioner           :11982  
                                          Median : 0.0000   Median : 157500   State servant       : 5044  
                                          Mean   : 0.4206   Mean   : 178867   Student             :    4  
                                          3rd Qu.: 1.0000   3rd Qu.: 225000   Working             :34944  
                                          Max.   :19.0000   Max.   :6750000                               
                                                                                                          
                    NAME_EDUCATION_TYPE            NAME_FAMILY_STATUS           NAME_HOUSING_TYPE
 Academic degree              :   38    Civil marriage      : 6016    Co-op apartment    :  227  
 Higher education             :16890    Married             :44906    House / apartment  :60307  
 Incomplete higher            : 2306    Separated           : 4125    Municipal apartment: 2303  
 Lower secondary              :  716    Single / not married: 9528    Office apartment   :  587  
 Secondary / secondary special:47664    Widow               : 3039    Rented apartment   : 1020  
                                                                      With parents       : 3170  
                                                                                                 
   DAYS_BIRTH     DAYS_EMPLOYED    FLAG_WORK_PHONE FLAG_PHONE FLAG_EMAIL    OCCUPATION_TYPE  CNT_FAM_MEMBERS 
 Min.   :-25201   Min.   :-17531   0:53904         0:49071    0:60819    Unknown    :20699   Min.   : 1.000  
 1st Qu.:-19438   1st Qu.: -2886   1:13710         1:18543    1: 6795    Laborers   :12425   1st Qu.: 2.000  
 Median :-15592   Median : -1305                                         Sales staff: 6899   Median : 2.000  
 Mean   :-15914   Mean   : 62022                                         Core staff : 6059   Mean   : 2.174  
 3rd Qu.:-12347   3rd Qu.:  -321                                         Managers   : 4950   3rd Qu.: 3.000  
 Max.   : -7489   Max.   :365243                                         Drivers    : 4427   Max.   :20.000  
                                                                         (Other)    :12155                   
     status     
 0      :52133  
 1      : 6491  
 7      : 5790  
 6      : 1805  
 2      :  712  
 5      :  374  
 (Other):  309  

Preprocessing

set.seed(713)
trainIndex <- initial_split(data, prop = 0.8, strata = status) 
trainingSet <- training(trainIndex)
testSet <- testing(trainIndex)

set.seed(12)
preprocRecipe <-
  recipe(status ~., data = trainingSet) %>%
  step_dummy(all_nominal()) %>%
  step_range(all_predictors(), -all_nominal(), min = 0, max = 1)

In this step the above defined receipt is extracted using the prep() function, and then use the bake() function to transform a set of data based on that recipe.

trainingSet_processed <- preprocRecipe %>%
  prep(trainingSet) %>%
  bake(trainingSet)
testSet_processed <- preprocRecipe %>%
  prep(testSet) %>%
  bake(testSet)

#OneHotEncoding

# # One-Hot-Encoding for CODE_GENDER
# dmy <- dummyVars(" ~ CODE_GENDER", data = data)
# trsf <- data.frame(predict(dmy, newdata = data)) 
# data <- cbind(data, trsf)
# data <- data %>% select(-CODE_GENDER)
# 
# # One-Hot-Encoding for FLAG_OWN_CAR 
# dmyCar <- dummyVars(" ~ FLAG_OWN_CAR", data = data)
# trsfCar <- data.frame(predict(dmyCar, newdata = data))
# data <- cbind(data, trsfCar)
# data <- data %>% select(-FLAG_OWN_CAR)
# 
# # One-Hot-Encoding for FLAG_OWN_REALTY   
# dmyRealty <- dummyVars(" ~ FLAG_OWN_REALTY", data = data)
# trsfRealty <- data.frame(predict(dmyRealty, newdata = data))
# data <- cbind(data, trsfRealty)
# data <- data %>% select(-FLAG_OWN_REALTY)
# 
# # One-Hot-Encoding for NAME_INCOME_TYPE   
# dmyNIT <- dummyVars(" ~ NAME_INCOME_TYPE", data = data)
# trsfNIT <- data.frame(predict(dmyNIT, newdata = data))
# data <- cbind(data, trsfNIT)
# data <- data %>% select(-NAME_INCOME_TYPE)
# 
# # Factoring NAME_EDUCATION_TYPE as it is ordinal
# # unique(data$NAME_EDUCATION_TYPE)
# # "Secondary / secondary special", "Higher education", "Incomplete higher", "Lower secondary", "Academic degree"
# # Ranking:
# # 'Lower secondary', 'Secondary / secondary special', 'Incomplete higher', 'Higher education', 'Academic degree'
# data$NAME_EDUCATION_TYPE <- as.numeric(factor(data$NAME_EDUCATION_TYPE, order=TRUE, levels=c('Lower secondary', 'Secondary / secondary special', 'Incomplete higher', 'Higher education', 'Academic degree')))
# #dmyNET <- dummyVars(" ~ NAME_EDUCATION_TYPE", data = data)
# #trsfNET <- data.frame(predict(dmyNET, newdata = data))
# #data <- cbind(data, trsfNET)
# #data <- data %>% select(-NAME_EDUCATION_TYPE)
# 
# # One-Hot-Encoding for NAME_FAMILY_STATUS   
# dmyNFS <- dummyVars(" ~ NAME_FAMILY_STATUS", data = data)
# trsfNFS <- data.frame(predict(dmyNFS, newdata = data))
# data <- cbind(data, trsfNFS)
# data <- data %>% select(-NAME_FAMILY_STATUS)
# 
# # One-Hot-Encoding for NAME_HOUSING_TYPE   
# dmyNHT <- dummyVars(" ~ NAME_HOUSING_TYPE", data = data)
# trsfNHT <- data.frame(predict(dmyNHT, newdata = data))
# data <- cbind(data, trsfNHT)
# data <- data %>% select(-NAME_HOUSING_TYPE)
# 
# # Remove FLAG_MOBIL, it is always 1
# #data <- data %>% select(-FLAG_MOBIL)
# 
# # One-Hot-Encoding for FLAG_WORK_PHONE  
# # Not needed, already 1 or 0
# # dmyFWP <- dummyVars(" ~ FLAG_WORK_PHONE", data = data)
# # trsfFWP <- data.frame(predict(dmyFWP, newdata = data))
# # data <- cbind(data, trsfFWP)
# # data <- data %>% select(-FLAG_WORK_PHONE)
# 
# # One-Hot-Encoding for FLAG_PHONE  
# # Not needed, already 1 or 0
# # dmyFP <- dummyVars(" ~ FLAG_PHONE", data = data)
# # trsfFP <- data.frame(predict(dmyFP, newdata = data))
# # data <- cbind(data, trsfFP)
# # data <- data %>% select(-FLAG_PHONE)
# 
# # One-Hot-Encoding for FLAG_EMAIL 
# # Not needed, already 1 or 0
# # dmyFE <- dummyVars(" ~ FLAG_EMAIL", data = data)
# # trsfFE <- data.frame(predict(dmyFE, newdata = data))
# # data <- cbind(data, trsfFE)
# # data <- data %>% select(-FLAG_EMAIL)
# 
# # One-Hot-Encoding for OCCUPATION_TYPE
# #data$OCCUPATION_TYPE
# #levels(data$OCCUPATION_TYPE)
# levels(data$OCCUPATION_TYPE) <- c(levels(data$OCCUPATION_TYPE), "Unknown")
# data$OCCUPATION_TYPE[is.na(data$OCCUPATION_TYPE)] <- "Unknown"
# dmyOT <- dummyVars(" ~ OCCUPATION_TYPE ", data = data)
# trsfOT <- data.frame(predict(dmyOT, newdata = data))
# data <- cbind(data, trsfOT)
# data <- data %>% select(-OCCUPATION_TYPE )
# 
# #summary(data)
# 
# ### Normalizing data
# 
# ## Does not work for single columns
# # preprocessParams <- preProcess(data$CNT_CHILDREN, method=c("center", "scale"))
# # summarize transform parameters
# # print(preprocessParams)
# # transform the dataset using the parameters
# # transformed <- predict(preprocessParams, iris[,1:4])
# 
# ## Does not work for single columns
# # mean <- apply(data, 2, mean)
# # std <- apply(data, 2, sd)
# # dataScaled <- scale(data$CNT_CHILDREN, center = mean, scale = std)
# 
# # ## Based on https://www.learnbymarketing.com/tutorials/neural-networks-in-r-tutorial/
# # #Might not be needed if done in preprocessing 
# # data$CNT_CHILDREN <- (data$CNT_CHILDREN-min(data$CNT_CHILDREN)) / (max(data$CNT_CHILDREN)-min(data$CNT_CHILDREN))
# # data$DAYS_BIRTH <- (data$DAYS_BIRTH-min(data$DAYS_BIRTH)) / (max(data$DAYS_BIRTH)-min(data$DAYS_BIRTH))
# # data$DAYS_EMPLOYED <- (data$DAYS_EMPLOYED-min(data$DAYS_EMPLOYED)) / (max(data$DAYS_EMPLOYED)-min(data$DAYS_EMPLOYED))
# # data$CNT_FAM_MEMBERS <- (data$CNT_FAM_MEMBERS-min(data$CNT_FAM_MEMBERS)) / (max(data$CNT_FAM_MEMBERS)-min(data$CNT_FAM_MEMBERS))
# 
# # ggplot(data = data, mapping = aes(x = ID, y = AMT_INCOME_TOTAL)) + geom_point()
# # heavily right skewed, but NN should not be affected by it
# #data$AMT_INCOME_TOTAL <- (data$AMT_INCOME_TOTAL-min(data$AMT_INCOME_TOTAL)) / (max(data$AMT_INCOME_TOTAL)-min(data$AMT_INCOME_TOTAL))
# 
# summary(data)

Check data

# dim(data)
# sapply(data, class)
# levels(data$status)
# 
# #Replace C and X in status
# #data$status[data$status == "C"] <- 6
# levels(data$status)[levels(data$status)=="C"] <- "6"
# #data$status[data$status == "X"] <- 7
# levels(data$status)[levels(data$status)=="X"] <- "7"
# #Convert factors into numericals
# data %<>% mutate_if(is.factor, as.numeric)
# levels(data$status)
# sapply(data, class)

# summarize the class distribution
percentage <- prop.table(table(data$status)) * 100
cbind(freq=table(data$status), percentage=percentage)

   freq percentage
0 52133 77.1038542
1  6491  9.6000828
2   712  1.0530364
3   195  0.2884018
4   114  0.1686041
5   374  0.5531399
6  1805  2.6695655
7  5790  8.5633153

# Turn data frame into data matrix
matrix_data <- trainingSet_processed %>% select(-tail(names(trainingSet_processed), 7))
#matrix_data <- subset(data, select = c(CNT_CHILDREN, AMT_INCOME_TOTAL))
#matrix_targets <- data.matrix(trainingSet_processed[])
matrix_targets <- trainingSet_processed %>% select(tail(names(trainingSet_processed), 7))

#Subset only 100 entries for testing
#matrix_data <- matrix_data[1:100, ]
#matrix_targets <- matrix_targets[1:100, ]

##K-Fold-Validation

#    # 75% of the sample size
#   smp_size <- floor(0.75 * nrow(data))
# 
#   # set the seed to make your partition reproducible
#   set.seed(123)
#   data_ind <- sample(seq_len(nrow(data)), size = smp_size)
#   train <- as_tibble(data[data_ind, ])
#   test <- as_tibble(data[-data_ind, ])
# 
#   train_no_status <- select(train,-c(status))
#   train_status <- select(train,c(status))
#   
# 
# #define model
# build_model <- function() {
#   model <- keras_model_sequential() %>%
#     layer_dense(units = 64, activation = "relu",
#                 input_shape = dim(train)[[2]]) %>%
#     layer_dense(units = 64, activation = "relu") %>%
#     layer_dense(units = 1)
# 
#   model %>% compile(
#     optimizer = "rmsprop",
#     loss = "mse",
#     metrics = c("mae")
#   )
# }

#   
# k <- 10
# indices <- sample(1:nrow(data))
# folds <- cut(indices, breaks = k, labels = FALSE)
# 
# num_epochs <- 100
# all_scores <- c()
# 
#  for (i in 1:k) {
# 
#   # Prepare the validation data: data from partition # k
#   val_indices <- which(folds == i, arr.ind = TRUE)
#   val_data <- data[val_indices,]
#   #val_targets <- train_status[val_indices,]
# 
#    # Prepare the training data: data from all other partitions
#   partial_train_data <- train_no_status[-val_indices,]
#   #partial_train_targets <- train_status[-val_indices,]
# 
#   #train & eval model
#   #model <- build_model()
# 
# 
#   #model %>% fit(partial_train_data, partial_train_targets,
#   #              epochs = num_epochs, batch_size = 1, verbose = 0)
# 
#   #validate
#   #results <- model %>% evaluate(val_data, val_targets, verbose = 0)
# 
#   #all_scores <- c(all_scores, results["mae"])
#  }

Build Model

  #Karsten's try
#train_data <- matrix_data
train_data <- data.matrix(matrix_data)
test_data <- data.matrix(matrix_data)
train_targets <- data.matrix(matrix_targets)

# Function to build the model
build_model <- function() {
  model <- keras_model_sequential() %>%
    layer_dense(units = 64, activation = "relu", input_shape = dim(train_data)[[2]]) %>%
    layer_dense(units = 64, activation = "relu") %>%
    layer_dense(units = 7, activation = "softmax")

  model %>% compile(
    optimizer = "rmsprop",
    loss = "categorical_crossentropy",
    metrics = c("accuracy")
  )
}

Train model

# mean <- apply(matrix_data, 2, mean)
# std <- apply(matrix_data, 2, sd)
# train_data <- scale(matrix_data, center = mean, scale = std)
# test_data <- scale(matrix_data, center = mean, scale = std)
# train_targets <- matrix_targets


k <- 2
indices <- sample(1:nrow(train_data))
folds <- cut(indices, breaks = k, labels = FALSE)

num_epochs <- 100
all_mae_histories <- NULL
for (i in 1:k) {
  cat("processing fold #", i, "\n")

  val_indices <- which(folds == i, arr.ind = TRUE)
  val_data <- train_data[val_indices,]
  val_targets <- train_targets[val_indices,]
  
  partial_train_data <- train_data[-val_indices,]
  partial_train_targets <- train_targets[-val_indices,]

  model <- build_model()

  # Train the model (in silent mode, verbose=0)
  # Batch size https://stats.stackexchange.com/questions/153531/what-is-batch-size-in-neural-network
  # One epoch = one forward pass and one backward pass of all the training examples
  # Batch size = the number of training examples in one forward/backward pass. The higher the batch size, the more memory space you'll need.
  # Number of iterations = number of passes, each pass using [batch size] number of examples. To be clear, one pass = one forward pass + one backward pass (we do not count the forward pass and backward pass as two different passes).
  # Batch size 32 much faster than 1, also the smaller the batch the less accurate the estimate of the gradient will be.
  history <- model %>% fit(
    partial_train_data, partial_train_targets,
    validation_data = list(val_data, val_targets),
    epochs = num_epochs, batch_size = 64, verbose = 1
  )
  mae_history <- history$metrics$val_mae
  all_mae_histories <- rbind(all_mae_histories, mae_history)
}

processing fold # 1 

2022-12-25 10:55:59.780175: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-12-25 10:56:00.592746: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 4617 MB memory:  -> device: 0, name: NVIDIA GeForce GTX 1060 6GB, pci bus id: 0000:2d:00.0, compute capability: 6.1

Epoch 1/100

  1/423 [..............................] - ETA: 7:26 - loss: 0.6600 - accuracy: 0.0469
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423/423 [==============================] - 2s 3ms/step - loss: 0.3845 - accuracy: 0.2724

423/423 [==============================] - 4s 8ms/step - loss: 0.3845 - accuracy: 0.2724 - val_loss: 0.5827 - val_accuracy: 0.8667
Epoch 2/100

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423/423 [==============================] - 1s 3ms/step - loss: 1.4610 - accuracy: 0.2817

423/423 [==============================] - 2s 5ms/step - loss: 1.4610 - accuracy: 0.2817 - val_loss: 1.7601 - val_accuracy: 0.0263
Epoch 3/100

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423/423 [==============================] - 1s 3ms/step - loss: 4.2999 - accuracy: 0.2292

423/423 [==============================] - 2s 6ms/step - loss: 4.2999 - accuracy: 0.2292 - val_loss: 4.1746 - val_accuracy: 0.8664
Epoch 4/100

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423/423 [==============================] - 1s 3ms/step - loss: 8.5301 - accuracy: 0.2253

423/423 [==============================] - 2s 5ms/step - loss: 8.5301 - accuracy: 0.2253 - val_loss: 5.1174 - val_accuracy: 0.8668
Epoch 5/100

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423/423 [==============================] - 1s 3ms/step - loss: 13.8513 - accuracy: 0.2234

423/423 [==============================] - 2s 5ms/step - loss: 13.8513 - accuracy: 0.2234 - val_loss: 21.7728 - val_accuracy: 0.0056
Epoch 6/100

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423/423 [==============================] - 1s 3ms/step - loss: 20.9432 - accuracy: 0.2248

423/423 [==============================] - 2s 5ms/step - loss: 20.9432 - accuracy: 0.2248 - val_loss: 29.1989 - val_accuracy: 0.0861
Epoch 7/100

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423/423 [==============================] - 2s 5ms/step - loss: 29.6181 - accuracy: 0.2194 - val_loss: 54.3074 - val_accuracy: 0.0056
Epoch 8/100

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423/423 [==============================] - 1s 3ms/step - loss: 39.8968 - accuracy: 0.2174

423/423 [==============================] - 2s 5ms/step - loss: 39.8968 - accuracy: 0.2174 - val_loss: 35.1052 - val_accuracy: 0.0861
Epoch 9/100

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423/423 [==============================] - 1s 3ms/step - loss: 49.9057 - accuracy: 0.2207

423/423 [==============================] - 2s 5ms/step - loss: 49.9057 - accuracy: 0.2207 - val_loss: 40.2671 - val_accuracy: 0.0030
Epoch 10/100

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423/423 [==============================] - 1s 3ms/step - loss: 62.8371 - accuracy: 0.2192

423/423 [==============================] - 2s 5ms/step - loss: 62.8371 - accuracy: 0.2192 - val_loss: 82.8960 - val_accuracy: 0.0056
Epoch 11/100

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423/423 [==============================] - 2s 5ms/step - loss: 75.0388 - accuracy: 0.2220 - val_loss: 117.9244 - val_accuracy: 0.0861
Epoch 12/100

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423/423 [==============================] - 1s 3ms/step - loss: 90.7634 - accuracy: 0.2227

423/423 [==============================] - 2s 5ms/step - loss: 90.7634 - accuracy: 0.2227 - val_loss: 95.0137 - val_accuracy: 0.0056
Epoch 13/100

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423/423 [==============================] - 1s 3ms/step - loss: 107.4899 - accuracy: 0.2198

423/423 [==============================] - 2s 5ms/step - loss: 107.4899 - accuracy: 0.2198 - val_loss: 109.7610 - val_accuracy: 0.8668
Epoch 14/100

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423/423 [==============================] - 2s 5ms/step - loss: 126.4646 - accuracy: 0.2201 - val_loss: 89.0703 - val_accuracy: 0.0861
Epoch 15/100

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423/423 [==============================] - 2s 5ms/step - loss: 142.5619 - accuracy: 0.2207 - val_loss: 88.2730 - val_accuracy: 0.8668
Epoch 16/100

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423/423 [==============================] - 1s 3ms/step - loss: 166.3860 - accuracy: 0.2220

423/423 [==============================] - 2s 5ms/step - loss: 166.3860 - accuracy: 0.2220 - val_loss: 127.5113 - val_accuracy: 0.8668
Epoch 17/100

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423/423 [==============================] - 1s 3ms/step - loss: 187.5859 - accuracy: 0.2202

423/423 [==============================] - 2s 5ms/step - loss: 187.5859 - accuracy: 0.2202 - val_loss: 51.3849 - val_accuracy: 0.8668
Epoch 18/100

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423/423 [==============================] - 1s 3ms/step - loss: 205.4121 - accuracy: 0.2206

423/423 [==============================] - 2s 5ms/step - loss: 205.4121 - accuracy: 0.2206 - val_loss: 237.9320 - val_accuracy: 0.0263
Epoch 19/100

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423/423 [==============================] - 2s 5ms/step - loss: 239.8756 - accuracy: 0.2216 - val_loss: 417.4932 - val_accuracy: 0.0263
Epoch 20/100

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423/423 [==============================] - 1s 3ms/step - loss: 263.0954 - accuracy: 0.2182

423/423 [==============================] - 2s 5ms/step - loss: 263.0954 - accuracy: 0.2182 - val_loss: 293.4865 - val_accuracy: 0.0056
Epoch 21/100

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423/423 [==============================] - 1s 3ms/step - loss: 285.0149 - accuracy: 0.2190

423/423 [==============================] - 2s 5ms/step - loss: 285.0149 - accuracy: 0.2190 - val_loss: 245.1297 - val_accuracy: 0.0030
Epoch 22/100

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423/423 [==============================] - 2s 4ms/step - loss: 318.3891 - accuracy: 0.2197

423/423 [==============================] - 2s 6ms/step - loss: 318.3891 - accuracy: 0.2197 - val_loss: 94.2687 - val_accuracy: 0.0861
Epoch 23/100

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423/423 [==============================] - 2s 4ms/step - loss: 347.2870 - accuracy: 0.2205

423/423 [==============================] - 3s 6ms/step - loss: 347.2870 - accuracy: 0.2205 - val_loss: 349.1081 - val_accuracy: 0.0861
Epoch 24/100

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423/423 [==============================] - 3s 6ms/step - loss: 376.9513 - accuracy: 0.2227 - val_loss: 568.1716 - val_accuracy: 0.0019
Epoch 25/100

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423/423 [==============================] - 3s 6ms/step - loss: 410.0341 - accuracy: 0.2223 - val_loss: 552.6620 - val_accuracy: 0.0019
Epoch 26/100

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423/423 [==============================] - 3s 6ms/step - loss: 436.5237 - accuracy: 0.2217 - val_loss: 279.4913 - val_accuracy: 0.0263
Epoch 27/100

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423/423 [==============================] - 3s 6ms/step - loss: 470.9351 - accuracy: 0.2216 - val_loss: 673.3672 - val_accuracy: 0.0056
Epoch 28/100

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423/423 [==============================] - 3s 6ms/step - loss: 515.3444 - accuracy: 0.2219 - val_loss: 672.8273 - val_accuracy: 0.0102
Epoch 29/100

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423/423 [==============================] - 3s 6ms/step - loss: 569.0831 - accuracy: 0.2204 - val_loss: 340.9089 - val_accuracy: 0.0085
Epoch 30/100

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423/423 [==============================] - 3s 7ms/step - loss: 614.1907 - accuracy: 0.2209 - val_loss: 1029.5057 - val_accuracy: 0.0019
Epoch 31/100

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423/423 [==============================] - 2s 4ms/step - loss: 634.5350 - accuracy: 0.2209

423/423 [==============================] - 3s 7ms/step - loss: 634.5350 - accuracy: 0.2209 - val_loss: 413.0065 - val_accuracy: 0.0030
Epoch 32/100

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423/423 [==============================] - 2s 5ms/step - loss: 664.8960 - accuracy: 0.2213

423/423 [==============================] - 3s 7ms/step - loss: 664.8960 - accuracy: 0.2213 - val_loss: 360.2962 - val_accuracy: 0.0861
Epoch 33/100

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423/423 [==============================] - 3s 7ms/step - loss: 728.7799 - accuracy: 0.2207 - val_loss: 576.5215 - val_accuracy: 0.0861
Epoch 34/100

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423/423 [==============================] - 2s 4ms/step - loss: 778.3900 - accuracy: 0.2211

423/423 [==============================] - 3s 7ms/step - loss: 778.3900 - accuracy: 0.2211 - val_loss: 754.8319 - val_accuracy: 0.0861
Epoch 35/100

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423/423 [==============================] - 2s 4ms/step - loss: 808.5696 - accuracy: 0.2200

423/423 [==============================] - 3s 7ms/step - loss: 808.5696 - accuracy: 0.2200 - val_loss: 633.2914 - val_accuracy: 0.8668
Epoch 36/100

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423/423 [==============================] - 3s 7ms/step - loss: 885.9993 - accuracy: 0.2234 - val_loss: 1537.8939 - val_accuracy: 0.0030
Epoch 37/100

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423/423 [==============================] - 3s 7ms/step - loss: 940.2429 - accuracy: 0.2194 - val_loss: 882.4721 - val_accuracy: 0.0102
Epoch 38/100

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423/423 [==============================] - 3s 7ms/step - loss: 955.8592 - accuracy: 0.2191 - val_loss: 452.6791 - val_accuracy: 0.0861
Epoch 39/100

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423/423 [==============================] - 3s 7ms/step - loss: 1012.2209 - accuracy: 0.2216 - val_loss: 942.3389 - val_accuracy: 0.0019
Epoch 40/100

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423/423 [==============================] - 2s 4ms/step - loss: 1076.3411 - accuracy: 0.2211

423/423 [==============================] - 3s 7ms/step - loss: 1076.3411 - accuracy: 0.2211 - val_loss: 1319.7783 - val_accuracy: 0.0861
Epoch 41/100

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423/423 [==============================] - 3s 7ms/step - loss: 1113.8972 - accuracy: 0.2201 - val_loss: 2059.7324 - val_accuracy: 0.0019
Epoch 42/100

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423/423 [==============================] - 2s 4ms/step - loss: 1190.0181 - accuracy: 0.2206

423/423 [==============================] - 3s 7ms/step - loss: 1190.0181 - accuracy: 0.2206 - val_loss: 1633.8035 - val_accuracy: 0.0102
Epoch 43/100

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423/423 [==============================] - 3s 7ms/step - loss: 1260.2096 - accuracy: 0.2202 - val_loss: 1644.5836 - val_accuracy: 0.0019
Epoch 44/100

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423/423 [==============================] - 3s 7ms/step - loss: 1305.7476 - accuracy: 0.2172 - val_loss: 2163.1199 - val_accuracy: 0.0056
Epoch 45/100

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423/423 [==============================] - 3s 7ms/step - loss: 1327.7344 - accuracy: 0.2234 - val_loss: 1404.8376 - val_accuracy: 0.0056
Epoch 46/100

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423/423 [==============================] - 3s 7ms/step - loss: 1422.4930 - accuracy: 0.2219 - val_loss: 1227.2670 - val_accuracy: 0.0056
Epoch 47/100

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423/423 [==============================] - 3s 7ms/step - loss: 1488.4928 - accuracy: 0.2217 - val_loss: 1950.4374 - val_accuracy: 0.0263
Epoch 48/100

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423/423 [==============================] - 2s 4ms/step - loss: 1532.7991 - accuracy: 0.2239

423/423 [==============================] - 3s 7ms/step - loss: 1532.7991 - accuracy: 0.2239 - val_loss: 1782.1453 - val_accuracy: 0.0861
Epoch 49/100

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423/423 [==============================] - 2s 4ms/step - loss: 1615.2163 - accuracy: 0.2216

423/423 [==============================] - 3s 7ms/step - loss: 1615.2163 - accuracy: 0.2216 - val_loss: 1697.1539 - val_accuracy: 0.0861
Epoch 50/100

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423/423 [==============================] - 3s 7ms/step - loss: 1654.7166 - accuracy: 0.2213 - val_loss: 1895.8971 - val_accuracy: 0.0056
Epoch 51/100

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423/423 [==============================] - 2s 4ms/step - loss: 1770.0402 - accuracy: 0.2196

423/423 [==============================] - 3s 7ms/step - loss: 1770.0402 - accuracy: 0.2196 - val_loss: 1826.6362 - val_accuracy: 0.0056
Epoch 52/100

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423/423 [==============================] - 2s 4ms/step - loss: 1808.6709 - accuracy: 0.2221

423/423 [==============================] - 3s 7ms/step - loss: 1808.6709 - accuracy: 0.2221 - val_loss: 931.2723 - val_accuracy: 0.0861
Epoch 53/100

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423/423 [==============================] - 2s 4ms/step - loss: 1893.5234 - accuracy: 0.2203

423/423 [==============================] - 3s 7ms/step - loss: 1893.5234 - accuracy: 0.2203 - val_loss: 953.0032 - val_accuracy: 0.8668
Epoch 54/100

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423/423 [==============================] - 3s 7ms/step - loss: 1962.8473 - accuracy: 0.2213 - val_loss: 1253.2781 - val_accuracy: 0.8668
Epoch 55/100

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423/423 [==============================] - 3s 7ms/step - loss: 2019.3547 - accuracy: 0.2240 - val_loss: 2389.2671 - val_accuracy: 0.0861
Epoch 56/100

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423/423 [==============================] - 3s 7ms/step - loss: 2135.0535 - accuracy: 0.2182 - val_loss: 3013.5408 - val_accuracy: 0.8668
Epoch 57/100

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423/423 [==============================] - 3s 7ms/step - loss: 2215.4563 - accuracy: 0.2201 - val_loss: 2163.1428 - val_accuracy: 0.0019
Epoch 58/100

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423/423 [==============================] - 3s 7ms/step - loss: 2246.9575 - accuracy: 0.2201 - val_loss: 3449.5173 - val_accuracy: 0.0102
Epoch 59/100

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423/423 [==============================] - 3s 7ms/step - loss: 2339.3120 - accuracy: 0.2226 - val_loss: 2603.4270 - val_accuracy: 0.0030
Epoch 60/100

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423/423 [==============================] - 3s 7ms/step - loss: 2434.4495 - accuracy: 0.2238 - val_loss: 1437.0094 - val_accuracy: 0.8668
Epoch 61/100

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423/423 [==============================] - 2s 4ms/step - loss: 2454.0967 - accuracy: 0.2171

423/423 [==============================] - 3s 7ms/step - loss: 2454.0967 - accuracy: 0.2171 - val_loss: 1740.5010 - val_accuracy: 0.0861
Epoch 62/100

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423/423 [==============================] - 2s 4ms/step - loss: 2540.0117 - accuracy: 0.2215

423/423 [==============================] - 3s 7ms/step - loss: 2540.0117 - accuracy: 0.2215 - val_loss: 2321.8850 - val_accuracy: 0.8668
Epoch 63/100

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423/423 [==============================] - 2s 4ms/step - loss: 2706.3958 - accuracy: 0.2213

423/423 [==============================] - 3s 7ms/step - loss: 2706.3958 - accuracy: 0.2213 - val_loss: 2697.2527 - val_accuracy: 0.8668
Epoch 64/100

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423/423 [==============================] - 2s 5ms/step - loss: 2800.0178 - accuracy: 0.2226

423/423 [==============================] - 3s 7ms/step - loss: 2800.0178 - accuracy: 0.2226 - val_loss: 4053.3301 - val_accuracy: 0.0030
Epoch 65/100

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423/423 [==============================] - 3s 7ms/step - loss: 2880.6775 - accuracy: 0.2175 - val_loss: 2599.2983 - val_accuracy: 0.8668
Epoch 66/100

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423/423 [==============================] - 2s 4ms/step - loss: 2885.4534 - accuracy: 0.2237

423/423 [==============================] - 3s 7ms/step - loss: 2885.4534 - accuracy: 0.2237 - val_loss: 1719.5421 - val_accuracy: 0.0019
Epoch 67/100

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423/423 [==============================] - 2s 4ms/step - loss: 2990.5850 - accuracy: 0.2233

423/423 [==============================] - 3s 7ms/step - loss: 2990.5850 - accuracy: 0.2233 - val_loss: 2500.3982 - val_accuracy: 0.0861
Epoch 68/100

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423/423 [==============================] - 3s 6ms/step - loss: 3067.3154 - accuracy: 0.2214 - val_loss: 2207.5884 - val_accuracy: 0.0030
Epoch 69/100

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423/423 [==============================] - 2s 4ms/step - loss: 3129.7556 - accuracy: 0.2232

423/423 [==============================] - 3s 7ms/step - loss: 3129.7556 - accuracy: 0.2232 - val_loss: 2338.3547 - val_accuracy: 0.8668
Epoch 70/100

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423/423 [==============================] - 3s 7ms/step - loss: 3347.5061 - accuracy: 0.2217 - val_loss: 3229.9541 - val_accuracy: 0.0102
Epoch 71/100

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423/423 [==============================] - 3s 7ms/step - loss: 3437.3970 - accuracy: 0.2203 - val_loss: 4011.4377 - val_accuracy: 0.8668
Epoch 72/100

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423/423 [==============================] - 3s 7ms/step - loss: 3569.8213 - accuracy: 0.2214 - val_loss: 3524.5798 - val_accuracy: 0.0019
Epoch 73/100

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423/423 [==============================] - 3s 7ms/step - loss: 3618.2791 - accuracy: 0.2257 - val_loss: 6349.1562 - val_accuracy: 0.0102
Epoch 74/100

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423/423 [==============================] - 3s 7ms/step - loss: 3664.1821 - accuracy: 0.2215 - val_loss: 2385.0879 - val_accuracy: 0.0056
Epoch 75/100

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423/423 [==============================] - 2s 4ms/step - loss: 3771.7561 - accuracy: 0.2196

423/423 [==============================] - 3s 7ms/step - loss: 3771.7561 - accuracy: 0.2196 - val_loss: 3218.0613 - val_accuracy: 0.0861
Epoch 76/100

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423/423 [==============================] - 2s 4ms/step - loss: 3936.6985 - accuracy: 0.2212

423/423 [==============================] - 3s 7ms/step - loss: 3936.6985 - accuracy: 0.2212 - val_loss: 3646.5093 - val_accuracy: 0.0861
Epoch 77/100

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423/423 [==============================] - 3s 7ms/step - loss: 4040.3943 - accuracy: 0.2209 - val_loss: 5348.7153 - val_accuracy: 0.0861
Epoch 78/100

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423/423 [==============================] - 3s 7ms/step - loss: 4037.2170 - accuracy: 0.2208 - val_loss: 4053.0840 - val_accuracy: 0.0263
Epoch 79/100

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423/423 [==============================] - 3s 7ms/step - loss: 4221.9390 - accuracy: 0.2204 - val_loss: 4343.5781 - val_accuracy: 0.0019
Epoch 80/100

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423/423 [==============================] - 3s 7ms/step - loss: 4170.8262 - accuracy: 0.2223 - val_loss: 3620.8667 - val_accuracy: 0.8668
Epoch 81/100

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423/423 [==============================] - 3s 7ms/step - loss: 4485.2681 - accuracy: 0.2211 - val_loss: 5294.4038 - val_accuracy: 0.0030
Epoch 82/100

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423/423 [==============================] - 2s 4ms/step - loss: 4509.9243 - accuracy: 0.2200

423/423 [==============================] - 3s 7ms/step - loss: 4509.9243 - accuracy: 0.2200 - val_loss: 4915.2622 - val_accuracy: 0.0861
Epoch 83/100

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423/423 [==============================] - 3s 7ms/step - loss: 4674.6807 - accuracy: 0.2199 - val_loss: 6673.5391 - val_accuracy: 0.0102
Epoch 84/100

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423/423 [==============================] - 3s 7ms/step - loss: 4837.2466 - accuracy: 0.2188 - val_loss: 5865.6704 - val_accuracy: 0.0263
Epoch 85/100

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423/423 [==============================] - 2s 4ms/step - loss: 4961.4751 - accuracy: 0.2181

423/423 [==============================] - 3s 7ms/step - loss: 4961.4751 - accuracy: 0.2181 - val_loss: 5024.3726 - val_accuracy: 0.0030
Epoch 86/100

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423/423 [==============================] - 3s 7ms/step - loss: 5038.7681 - accuracy: 0.2224 - val_loss: 4861.9399 - val_accuracy: 0.0861
Epoch 87/100

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423/423 [==============================] - 3s 7ms/step - loss: 5094.6016 - accuracy: 0.2220 - val_loss: 5445.4307 - val_accuracy: 0.0861
Epoch 88/100

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423/423 [==============================] - 3s 7ms/step - loss: 5230.3120 - accuracy: 0.2229 - val_loss: 8440.4355 - val_accuracy: 0.0030
Epoch 89/100

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423/423 [==============================] - 3s 7ms/step - loss: 5254.4727 - accuracy: 0.2218 - val_loss: 7596.7500 - val_accuracy: 0.0030
Epoch 90/100

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423/423 [==============================] - 3s 6ms/step - loss: 5503.9102 - accuracy: 0.2197 - val_loss: 4664.5571 - val_accuracy: 0.8668
Epoch 91/100

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423/423 [==============================] - 3s 7ms/step - loss: 5594.3291 - accuracy: 0.2250 - val_loss: 6085.7354 - val_accuracy: 0.0056
Epoch 92/100

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423/423 [==============================] - 3s 7ms/step - loss: 5762.5112 - accuracy: 0.2199 - val_loss: 6254.5234 - val_accuracy: 0.0056
Epoch 93/100

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423/423 [==============================] - 3s 7ms/step - loss: 5941.3965 - accuracy: 0.2229 - val_loss: 6711.1548 - val_accuracy: 0.0030
Epoch 94/100

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423/423 [==============================] - 2s 4ms/step - loss: 6029.2563 - accuracy: 0.2202

423/423 [==============================] - 3s 7ms/step - loss: 6029.2563 - accuracy: 0.2202 - val_loss: 2573.6509 - val_accuracy: 0.8668
Epoch 95/100

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423/423 [==============================] - 2s 4ms/step - loss: 6210.9077 - accuracy: 0.2229

423/423 [==============================] - 3s 7ms/step - loss: 6210.9077 - accuracy: 0.2229 - val_loss: 5740.5317 - val_accuracy: 0.0056
Epoch 96/100

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423/423 [==============================] - 2s 4ms/step - loss: 6228.9932 - accuracy: 0.2217

423/423 [==============================] - 3s 7ms/step - loss: 6228.9932 - accuracy: 0.2217 - val_loss: 5585.9302 - val_accuracy: 0.0102
Epoch 97/100

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423/423 [==============================] - 2s 4ms/step - loss: 6203.1274 - accuracy: 0.2228

423/423 [==============================] - 3s 7ms/step - loss: 6203.1274 - accuracy: 0.2228 - val_loss: 5511.9551 - val_accuracy: 0.0102
Epoch 98/100

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423/423 [==============================] - 3s 7ms/step - loss: 6459.5601 - accuracy: 0.2195 - val_loss: 6811.0264 - val_accuracy: 0.0030
Epoch 99/100

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423/423 [==============================] - 3s 7ms/step - loss: 6720.3271 - accuracy: 0.2220 - val_loss: 6862.3955 - val_accuracy: 0.8668
Epoch 100/100

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423/423 [==============================] - 3s 7ms/step - loss: 6847.0273 - accuracy: 0.2229 - val_loss: 8753.7412 - val_accuracy: 0.0102
processing fold # 2 
Epoch 1/100

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423/423 [==============================] - 4s 8ms/step - loss: 0.3744 - accuracy: 0.5735 - val_loss: 0.7960 - val_accuracy: 0.0362
Epoch 2/100

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423/423 [==============================] - 3s 7ms/step - loss: 1.4432 - accuracy: 0.2824 - val_loss: 3.1237 - val_accuracy: 0.0256
Epoch 3/100

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423/423 [==============================] - 2s 4ms/step - loss: 4.4816 - accuracy: 0.2280

423/423 [==============================] - 3s 7ms/step - loss: 4.4816 - accuracy: 0.2280 - val_loss: 8.9806 - val_accuracy: 0.8696
Epoch 4/100

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423/423 [==============================] - 2s 4ms/step - loss: 8.8362 - accuracy: 0.2208

423/423 [==============================] - 3s 6ms/step - loss: 8.8362 - accuracy: 0.2208 - val_loss: 13.1770 - val_accuracy: 0.8696
Epoch 5/100

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423/423 [==============================] - 3s 7ms/step - loss: 14.5881 - accuracy: 0.2200 - val_loss: 19.9934 - val_accuracy: 0.8696
Epoch 6/100

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423/423 [==============================] - 3s 7ms/step - loss: 21.6099 - accuracy: 0.2200 - val_loss: 43.9245 - val_accuracy: 0.0256
Epoch 7/100

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423/423 [==============================] - 3s 7ms/step - loss: 30.2671 - accuracy: 0.2165 - val_loss: 43.6499 - val_accuracy: 0.0847
Epoch 8/100

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423/423 [==============================] - 3s 7ms/step - loss: 39.2564 - accuracy: 0.2214 - val_loss: 21.3789 - val_accuracy: 0.0847
Epoch 9/100

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423/423 [==============================] - 3s 7ms/step - loss: 51.6239 - accuracy: 0.2196 - val_loss: 87.2402 - val_accuracy: 0.0847
Epoch 10/100

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423/423 [==============================] - 3s 8ms/step - loss: 63.3565 - accuracy: 0.2176 - val_loss: 35.2002 - val_accuracy: 0.0847
Epoch 11/100

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423/423 [==============================] - 3s 6ms/step - loss: 78.9472 - accuracy: 0.2207 - val_loss: 48.5626 - val_accuracy: 0.8696
Epoch 12/100

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423/423 [==============================] - 3s 7ms/step - loss: 93.4451 - accuracy: 0.2240 - val_loss: 144.4121 - val_accuracy: 0.0016
Epoch 13/100

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423/423 [==============================] - 3s 7ms/step - loss: 111.7713 - accuracy: 0.2191 - val_loss: 121.5458 - val_accuracy: 0.0016
Epoch 14/100

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423/423 [==============================] - 3s 7ms/step - loss: 127.4752 - accuracy: 0.2176 - val_loss: 160.4992 - val_accuracy: 0.8696
Epoch 15/100

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423/423 [==============================] - 3s 7ms/step - loss: 146.9290 - accuracy: 0.2189 - val_loss: 110.5549 - val_accuracy: 0.0028
Epoch 16/100

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423/423 [==============================] - 2s 4ms/step - loss: 168.6277 - accuracy: 0.2199

423/423 [==============================] - 3s 6ms/step - loss: 168.6277 - accuracy: 0.2199 - val_loss: 163.8787 - val_accuracy: 0.0256
Epoch 17/100

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423/423 [==============================] - 2s 4ms/step - loss: 191.6816 - accuracy: 0.2168

423/423 [==============================] - 3s 6ms/step - loss: 191.6816 - accuracy: 0.2168 - val_loss: 78.0703 - val_accuracy: 0.8696
Epoch 18/100

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423/423 [==============================] - 3s 7ms/step - loss: 219.6160 - accuracy: 0.2191 - val_loss: 136.4899 - val_accuracy: 0.8696
Epoch 19/100

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423/423 [==============================] - 2s 4ms/step - loss: 239.0346 - accuracy: 0.2194

423/423 [==============================] - 3s 7ms/step - loss: 239.0346 - accuracy: 0.2194 - val_loss: 245.1090 - val_accuracy: 0.0107
Epoch 20/100

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423/423 [==============================] - 3s 7ms/step - loss: 266.6973 - accuracy: 0.2193 - val_loss: 508.6523 - val_accuracy: 0.0107
Epoch 21/100

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423/423 [==============================] - 3s 7ms/step - loss: 289.9398 - accuracy: 0.2180 - val_loss: 537.0037 - val_accuracy: 0.0050
Epoch 22/100

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423/423 [==============================] - 3s 7ms/step - loss: 325.4903 - accuracy: 0.2186 - val_loss: 430.6736 - val_accuracy: 0.0016
Epoch 23/100

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423/423 [==============================] - 3s 7ms/step - loss: 351.9172 - accuracy: 0.2174 - val_loss: 705.3685 - val_accuracy: 0.0050
Epoch 24/100

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423/423 [==============================] - 3s 7ms/step - loss: 398.0149 - accuracy: 0.2193 - val_loss: 601.3679 - val_accuracy: 0.0256
Epoch 25/100

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423/423 [==============================] - 3s 7ms/step - loss: 424.0538 - accuracy: 0.2170 - val_loss: 429.0845 - val_accuracy: 0.0050
Epoch 26/100

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423/423 [==============================] - 3s 6ms/step - loss: 465.0021 - accuracy: 0.2195 - val_loss: 545.9169 - val_accuracy: 0.0016
Epoch 27/100

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423/423 [==============================] - 3s 7ms/step - loss: 491.9820 - accuracy: 0.2171 - val_loss: 475.9216 - val_accuracy: 0.0847
Epoch 28/100

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423/423 [==============================] - 3s 7ms/step - loss: 533.4672 - accuracy: 0.2162 - val_loss: 743.0394 - val_accuracy: 0.0028
Epoch 29/100

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423/423 [==============================] - 3s 6ms/step - loss: 583.8863 - accuracy: 0.2174 - val_loss: 553.8320 - val_accuracy: 0.0050
Epoch 30/100

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423/423 [==============================] - 3s 6ms/step - loss: 602.0250 - accuracy: 0.2214 - val_loss: 823.1324 - val_accuracy: 0.0050
Epoch 31/100

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423/423 [==============================] - 3s 6ms/step - loss: 641.4816 - accuracy: 0.2176 - val_loss: 549.6638 - val_accuracy: 0.8696
Epoch 32/100

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423/423 [==============================] - 3s 6ms/step - loss: 701.1481 - accuracy: 0.2206 - val_loss: 754.5574 - val_accuracy: 0.0107
Epoch 33/100

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423/423 [==============================] - 3s 6ms/step - loss: 749.6153 - accuracy: 0.2165 - val_loss: 864.4152 - val_accuracy: 0.0256
Epoch 34/100

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423/423 [==============================] - 3s 6ms/step - loss: 796.5964 - accuracy: 0.2193 - val_loss: 867.0770 - val_accuracy: 0.0847
Epoch 35/100

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423/423 [==============================] - 3s 6ms/step - loss: 833.6522 - accuracy: 0.2194 - val_loss: 1122.0848 - val_accuracy: 0.0107
Epoch 36/100

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423/423 [==============================] - 3s 6ms/step - loss: 908.9216 - accuracy: 0.2156 - val_loss: 1114.0831 - val_accuracy: 0.0050
Epoch 37/100

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423/423 [==============================] - 3s 6ms/step - loss: 933.1898 - accuracy: 0.2174 - val_loss: 760.2424 - val_accuracy: 0.0028
Epoch 38/100

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423/423 [==============================] - 2s 4ms/step - loss: 967.4230 - accuracy: 0.2167

423/423 [==============================] - 3s 6ms/step - loss: 967.4230 - accuracy: 0.2167 - val_loss: 270.6997 - val_accuracy: 0.8696
Epoch 39/100

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423/423 [==============================] - 3s 6ms/step - loss: 1015.2068 - accuracy: 0.2206 - val_loss: 1529.5438 - val_accuracy: 0.0016
Epoch 40/100

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423/423 [==============================] - 3s 6ms/step - loss: 1091.5245 - accuracy: 0.2152 - val_loss: 1704.8323 - val_accuracy: 0.0256
Epoch 41/100

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423/423 [==============================] - 3s 6ms/step - loss: 1183.9520 - accuracy: 0.2189 - val_loss: 1493.1807 - val_accuracy: 0.0050
Epoch 42/100

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423/423 [==============================] - 3s 6ms/step - loss: 1216.0237 - accuracy: 0.2170 - val_loss: 2100.3110 - val_accuracy: 0.0016
Epoch 43/100

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423/423 [==============================] - 3s 7ms/step - loss: 1288.6104 - accuracy: 0.2166 - val_loss: 811.1714 - val_accuracy: 0.0050
Epoch 44/100

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423/423 [==============================] - 2s 4ms/step - loss: 1335.4014 - accuracy: 0.2193

423/423 [==============================] - 3s 7ms/step - loss: 1335.4014 - accuracy: 0.2193 - val_loss: 695.5342 - val_accuracy: 0.0847
Epoch 45/100

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423/423 [==============================] - 3s 7ms/step - loss: 1336.8466 - accuracy: 0.2188 - val_loss: 1586.8085 - val_accuracy: 0.0256
Epoch 46/100

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423/423 [==============================] - 2s 4ms/step - loss: 1433.2089 - accuracy: 0.2192

423/423 [==============================] - 3s 7ms/step - loss: 1433.2089 - accuracy: 0.2192 - val_loss: 1115.2219 - val_accuracy: 0.0050
Epoch 47/100

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423/423 [==============================] - 3s 6ms/step - loss: 1511.8854 - accuracy: 0.2202 - val_loss: 2508.4006 - val_accuracy: 0.0107
Epoch 48/100

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423/423 [==============================] - 3s 6ms/step - loss: 1583.6993 - accuracy: 0.2183 - val_loss: 1498.5984 - val_accuracy: 0.0847
Epoch 49/100

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423/423 [==============================] - 2s 4ms/step - loss: 1687.9618 - accuracy: 0.2193

423/423 [==============================] - 3s 6ms/step - loss: 1687.9618 - accuracy: 0.2193 - val_loss: 2635.5645 - val_accuracy: 0.0050
Epoch 50/100

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423/423 [==============================] - 3s 6ms/step - loss: 1747.6094 - accuracy: 0.2160 - val_loss: 1599.9138 - val_accuracy: 0.8696
Epoch 51/100

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423/423 [==============================] - 3s 6ms/step - loss: 1755.6244 - accuracy: 0.2216 - val_loss: 2802.2380 - val_accuracy: 0.0016
Epoch 52/100

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423/423 [==============================] - 2s 4ms/step - loss: 1866.3090 - accuracy: 0.2181

423/423 [==============================] - 3s 6ms/step - loss: 1866.3090 - accuracy: 0.2181 - val_loss: 2337.7385 - val_accuracy: 0.8696
Epoch 53/100

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423/423 [==============================] - 3s 6ms/step - loss: 1928.8986 - accuracy: 0.2188 - val_loss: 2663.6577 - val_accuracy: 0.0847
Epoch 54/100

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423/423 [==============================] - 3s 6ms/step - loss: 2009.6316 - accuracy: 0.2211 - val_loss: 4087.9370 - val_accuracy: 0.0028
Epoch 55/100

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423/423 [==============================] - 3s 6ms/step - loss: 2143.8667 - accuracy: 0.2173 - val_loss: 3809.3518 - val_accuracy: 0.0050
Epoch 56/100

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423/423 [==============================] - 2s 4ms/step - loss: 2188.9351 - accuracy: 0.2216

423/423 [==============================] - 3s 6ms/step - loss: 2188.9351 - accuracy: 0.2216 - val_loss: 2982.1580 - val_accuracy: 0.0107
Epoch 57/100

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423/423 [==============================] - 3s 6ms/step - loss: 2265.2048 - accuracy: 0.2188 - val_loss: 2268.0676 - val_accuracy: 0.0050
Epoch 58/100

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423/423 [==============================] - 3s 6ms/step - loss: 2254.5945 - accuracy: 0.2180 - val_loss: 2941.6572 - val_accuracy: 0.0016
Epoch 59/100

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423/423 [==============================] - 3s 6ms/step - loss: 2408.0000 - accuracy: 0.2194 - val_loss: 1716.7976 - val_accuracy: 0.0847
Epoch 60/100

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423/423 [==============================] - 3s 6ms/step - loss: 2520.3589 - accuracy: 0.2159 - val_loss: 2354.6445 - val_accuracy: 0.0028
Epoch 61/100

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423/423 [==============================] - 3s 7ms/step - loss: 2517.9333 - accuracy: 0.2196 - val_loss: 2040.2244 - val_accuracy: 0.0028
Epoch 62/100

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423/423 [==============================] - 3s 7ms/step - loss: 2632.3928 - accuracy: 0.2208 - val_loss: 1389.5411 - val_accuracy: 0.0847
Epoch 63/100

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423/423 [==============================] - 3s 7ms/step - loss: 2743.8464 - accuracy: 0.2150 - val_loss: 1468.5592 - val_accuracy: 0.0107
Epoch 64/100

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423/423 [==============================] - 3s 6ms/step - loss: 2835.0901 - accuracy: 0.2176 - val_loss: 2543.5896 - val_accuracy: 0.0847
Epoch 65/100

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423/423 [==============================] - 3s 6ms/step - loss: 2882.1829 - accuracy: 0.2188 - val_loss: 2136.2598 - val_accuracy: 0.0847
Epoch 66/100

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423/423 [==============================] - 3s 6ms/step - loss: 3017.6062 - accuracy: 0.2200 - val_loss: 3379.0095 - val_accuracy: 0.0847
Epoch 67/100

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423/423 [==============================] - 3s 6ms/step - loss: 3151.0623 - accuracy: 0.2179 - val_loss: 2128.5957 - val_accuracy: 0.8696
Epoch 68/100

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423/423 [==============================] - 3s 6ms/step - loss: 3152.4072 - accuracy: 0.2210 - val_loss: 4713.6641 - val_accuracy: 0.0016
Epoch 69/100

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423/423 [==============================] - 3s 7ms/step - loss: 3303.7231 - accuracy: 0.2179 - val_loss: 2378.8728 - val_accuracy: 0.0050
Epoch 70/100

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423/423 [==============================] - 3s 7ms/step - loss: 3346.1626 - accuracy: 0.2209 - val_loss: 1879.6941 - val_accuracy: 0.8696
Epoch 71/100

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423/423 [==============================] - 3s 6ms/step - loss: 3452.6362 - accuracy: 0.2181 - val_loss: 3194.7585 - val_accuracy: 0.0050
Epoch 72/100

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423/423 [==============================] - 3s 7ms/step - loss: 3515.6025 - accuracy: 0.2199 - val_loss: 3315.8501 - val_accuracy: 0.0847
Epoch 73/100

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423/423 [==============================] - 2s 4ms/step - loss: 3721.1628 - accuracy: 0.2185

423/423 [==============================] - 3s 7ms/step - loss: 3721.1628 - accuracy: 0.2185 - val_loss: 2585.6218 - val_accuracy: 0.0847
Epoch 74/100

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423/423 [==============================] - 2s 4ms/step - loss: 3819.3733 - accuracy: 0.2186

423/423 [==============================] - 3s 6ms/step - loss: 3819.3733 - accuracy: 0.2186 - val_loss: 4387.7495 - val_accuracy: 0.0256
Epoch 75/100

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423/423 [==============================] - 3s 6ms/step - loss: 3937.0085 - accuracy: 0.2190 - val_loss: 3245.5220 - val_accuracy: 0.0847
Epoch 76/100

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423/423 [==============================] - 3s 7ms/step - loss: 4053.8232 - accuracy: 0.2166 - val_loss: 6530.5156 - val_accuracy: 0.0107
Epoch 77/100

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423/423 [==============================] - 3s 6ms/step - loss: 4246.3135 - accuracy: 0.2176 - val_loss: 3660.9231 - val_accuracy: 0.8696
Epoch 78/100

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423/423 [==============================] - 3s 6ms/step - loss: 4207.9668 - accuracy: 0.2190 - val_loss: 6436.2686 - val_accuracy: 0.0016
Epoch 79/100

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423/423 [==============================] - 3s 6ms/step - loss: 4487.6899 - accuracy: 0.2190 - val_loss: 4989.6772 - val_accuracy: 0.0256
Epoch 80/100

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423/423 [==============================] - 3s 6ms/step - loss: 4555.5640 - accuracy: 0.2203 - val_loss: 6231.8076 - val_accuracy: 0.0107
Epoch 81/100

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423/423 [==============================] - 3s 6ms/step - loss: 4640.9985 - accuracy: 0.2197 - val_loss: 3396.9563 - val_accuracy: 0.0847
Epoch 82/100

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423/423 [==============================] - 3s 6ms/step - loss: 4524.5073 - accuracy: 0.2201 - val_loss: 5555.2793 - val_accuracy: 0.0107
Epoch 83/100

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423/423 [==============================] - 3s 6ms/step - loss: 4874.4824 - accuracy: 0.2195 - val_loss: 5603.1519 - val_accuracy: 0.0028
Epoch 84/100

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423/423 [==============================] - 3s 6ms/step - loss: 4794.4526 - accuracy: 0.2204 - val_loss: 7258.4395 - val_accuracy: 0.0256
Epoch 85/100

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423/423 [==============================] - 2s 4ms/step - loss: 5063.0391 - accuracy: 0.2197

423/423 [==============================] - 3s 6ms/step - loss: 5063.0391 - accuracy: 0.2197 - val_loss: 3285.5444 - val_accuracy: 0.0847
Epoch 86/100

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423/423 [==============================] - 3s 7ms/step - loss: 5072.5967 - accuracy: 0.2207 - val_loss: 7271.4302 - val_accuracy: 0.0050
Epoch 87/100

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423/423 [==============================] - 3s 6ms/step - loss: 5221.0439 - accuracy: 0.2189 - val_loss: 1510.4651 - val_accuracy: 0.0107
Epoch 88/100

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423/423 [==============================] - 2s 4ms/step - loss: 5288.6196 - accuracy: 0.2208

423/423 [==============================] - 3s 7ms/step - loss: 5288.6196 - accuracy: 0.2208 - val_loss: 6100.8403 - val_accuracy: 0.0256
Epoch 89/100

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423/423 [==============================] - 3s 6ms/step - loss: 5620.5464 - accuracy: 0.2178 - val_loss: 5029.8135 - val_accuracy: 0.0847
Epoch 90/100

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423/423 [==============================] - 3s 7ms/step - loss: 5631.1313 - accuracy: 0.2185 - val_loss: 8735.3896 - val_accuracy: 0.0256
Epoch 91/100

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423/423 [==============================] - 3s 7ms/step - loss: 5799.0273 - accuracy: 0.2154 - val_loss: 5452.1128 - val_accuracy: 0.8696
Epoch 92/100

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423/423 [==============================] - 3s 7ms/step - loss: 5878.4185 - accuracy: 0.2174 - val_loss: 5824.4536 - val_accuracy: 0.0256
Epoch 93/100

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423/423 [==============================] - 3s 7ms/step - loss: 6082.3735 - accuracy: 0.2197 - val_loss: 3271.7612 - val_accuracy: 0.0050
Epoch 94/100

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423/423 [==============================] - 3s 7ms/step - loss: 6228.0396 - accuracy: 0.2177 - val_loss: 10841.5088 - val_accuracy: 0.0016
Epoch 95/100

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423/423 [==============================] - 3s 7ms/step - loss: 6249.2900 - accuracy: 0.2186 - val_loss: 8981.4951 - val_accuracy: 0.0016
Epoch 96/100

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423/423 [==============================] - 3s 7ms/step - loss: 6473.8735 - accuracy: 0.2177 - val_loss: 9493.5303 - val_accuracy: 0.0016
Epoch 97/100

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423/423 [==============================] - 3s 7ms/step - loss: 6557.0361 - accuracy: 0.2155 - val_loss: 4050.1682 - val_accuracy: 0.8696
Epoch 98/100

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423/423 [==============================] - 3s 7ms/step - loss: 6844.8413 - accuracy: 0.2172 - val_loss: 5504.6377 - val_accuracy: 0.0256
Epoch 99/100

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423/423 [==============================] - 3s 7ms/step - loss: 6749.2266 - accuracy: 0.2188 - val_loss: 2976.2046 - val_accuracy: 0.0107
Epoch 100/100

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398/423 [===========================>..] - ETA: 0s - loss: 7006.0356 - accuracy: 0.2204
412/423 [============================>.] - ETA: 0s - loss: 6968.0073 - accuracy: 0.2202
423/423 [==============================] - 2s 4ms/step - loss: 6959.5825 - accuracy: 0.2194

423/423 [==============================] - 3s 7ms/step - loss: 6959.5825 - accuracy: 0.2194 - val_loss: 5472.1870 - val_accuracy: 0.0256

#reticulate::py_last_error()

#We can then compute the average of the per-epoch MAE scores for all folds:

average_mae_history <- data.frame(
  epoch = seq(1:ncol(all_mae_histories)),
  validation_mae = apply(all_mae_histories, 2, mean)
)

Error in 1:ncol(all_mae_histories) : argument of length 0

# all_scores
# mean(all_scores)

  ## Take Model1 and Scale data
  ##scale and preprocess training and test data
  #library(caret)
  pre_proc_val <- preProcess(train, method = c("center", "scale"))  
  train = predict(pre_proc_val, train)
  test = predict(pre_proc_val, test)
  train <- tensor_slices_dataset(train)
  test <- tensor_slices_dataset(test)
  summary(train)
  

str(train_data[[1]])